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06/12/2023
Ensemble-based Offline-to-Online Reinforcement Learning: From Pessimistic Learning to Optimistic Exploration
Offline reinforcement learning (RL) is a learning paradigm where an agen...
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06/10/2023
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach
Offline reinforcement learning (ORL) has gained attention as a means of ...
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06/09/2023
In-Sample Policy Iteration for Offline Reinforcement Learning
Offline reinforcement learning (RL) seeks to derive an effective control...
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10/26/2022
ERL-Re^2: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation
Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithm (EA) ar...
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04/06/2022
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
Deep Reinforcement Learning (DRL) has been a promising solution to many ...
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11/19/2021
Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning
Value estimation is one key problem in Reinforcement Learning. Albeit ma...
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09/14/2021
Exploration in Deep Reinforcement Learning: A Comprehensive Survey
Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Lea...
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09/12/2021
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Discrete-continuous hybrid action space is a natural setting in many pra...
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04/20/2021
Free-form tumor synthesis in computed tomography images via richer generative adversarial network
The insufficiency of annotated medical imaging scans for cancer makes it...
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04/20/2021
Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images
The capability of generalization to unseen domains is crucial for deep l...
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05/14/2020
Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management
With the increasing popularity of electric vehicles, distributed energy ...
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02/19/2020
Efficient Deep Reinforcement Learning through Policy Transfer
Transfer Learning (TL) has shown great potential to accelerate Reinforce...
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02/19/2020
Learning When to Transfer among Agents: An Efficient Multiagent Transfer Learning Framework
Transfer Learning has shown great potential to enhance the single-agent ...
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05/27/2019
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Value functions are crucial for model-free Reinforcement Learning (RL) t...
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11/04/2018
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
Automatic extraction of liver and tumor from CT volumes is a challenging...
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11/03/2018
DUNet: A deformable network for retinal vessel segmentation
Automatic segmentation of retinal vessels in fundus images plays an impo...
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09/12/2018
Bayes-ToMoP: A Fast Detection and Best Response Algorithm Towards Sophisticated Opponents
Multiagent algorithms often aim to accurately predict the behaviors of o...
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03/08/2018
